MODULE - 1 : Analytical tools and applications

Module Duration:

40 hours of recorded videos and 8 live online interactive sessions.

About the Module:

The module introduces the basic concepts such as optimization and game theory to the managers. These concepts are extensively used in subsequent modules. The module also introduces the fundamentals of probability and statistics. The entire module 2 (decision making under uncertainty) is based on these concepts. The difference between an optimal solution and an equilibrium is explained using a two-person game.

Pre-requisite for the Module :

Basic background in mathematics and logical thinking. Even though the basics of statistics are introduced in the module, a flair for empirical approaches will surely help. 

Learning Outcomes:

• Describe the basics of optimization
• Identify the decision variables, constraints and objectives of an optimization problem
• Solve basic problems in statistics
• Explain the concept of equilibrium in a multi agent setup.

Applications of the Module:

The analytical tools covered in this module are the most commonly used methods to solve basic analytical problems. Without a clear understanding of these tools and theory supporting these tools, it is difficult to appreciate nuances of the future concepts. These tools are the building blocks for any analytical model used in industry. It is strongly encouraged that the participants make themselves familiar with these tools and have a firm grasp of the implementation challenges.

MODULE - 2 : Decision making under uncertainty

Module Duration :

40 hours of recorded video and 8 live online interactive sessions.

About the Module:

Two main challenges in any industry or an organization are decision making over time, and under uncertainty. Time dimension and uncertainty in decision making are the focus of this module. The various tools appropriate for incorporating time and uncertainty are discussed in this module. The necessary theoretical understanding is also built in, with the focus remaining on the application driven approach. Tools like simulation are discussed and their applications explained.

Pre- requisite for the Module:

Fundamentals of optimization, statistics and probability. The participants should have solved basic formulations in optimization (linear, non-linear and integer programming). It is also expected that the participants are familiar with probability theory (probability distributions, descriptive and inferential statistics).

Learning Outcomes:

• Define the nature of uncertainty and apply the same in the analytical models
• Recognize the time based decision making and provision for the same
• Select appropriate probabilistic constraints in the formulations
• Built simulation model and analyze them theoretically

Applications of the Module:

We live in a VUCA- (Volatility, Uncertainty, Complexity and Ambiguity) - world. Every organization and industry, and even our personal lives are governed by uncertainty. Similarly, the longevity of our decisions need to be analyzed carefully – taking a decision for a situation further in time may be a complex process. Therefore, every manager must master the tools that help us consider the time dimension of our decisions. Similarly, volatility in the environment needs to be modeled – in the form of variables and constraints – in our model. The biggest takeaway from this module will be appreciating the importance of building robust models – that can stand the test of time and volatility

MODULE 3 : Cases and applications

Module Duration:

40 hours of recorded videos and 8 live online interactive sessions.

About the Module:

The analytical tools of optimization, game theory, statistics and simulation are theoretically flexible and generic so that they can be employed in a variety of situations, organizations, industries and economies. In this module, we demonstrate the applications of these analytical tools in various areas of decision making. We take cases from diverse industries such as manufacturing, services, airline, logistics and build analytical models that can support strategic decision making. The focus of this module is to prepare the participants to build analytical models on their own, by learning from these cases.

Pre-requisites for the Module :

It is expected that the participants are familiar with various analytical modeling tools (optimization, probability, game theory). They should have applied analytical models to some scenarios before coming to this module. They should have a functional understanding of the theoretical underpinning and limitations of the analytical tools so that appropriate tools can be applied correctly to a given situation. Module 1 and 2 of this program are designed so that the participants can build the required background for this module. However, the participants with the requisite skills can register for this module directly.

Learning Outcomes:

• Organize the domain specific elements in the analytical model
• Compare the models discussed in the module with your situation and make necessary changes
• Apply the building blocks discussed in the module to your organization and industry
• Critique various models for their applications across industries
• Appreciate the generality of the tools discussed and make the tools specific to your needs
• Build models that fit your situations

Applications of the Module :

One of the most important skills as an analyst is to be able to convert business situations into mathematical models; and also to draw insightful conclusions from the mathematical models in the business language. This module discusses analytical models applied to various industries and encourages the participants to find suitable applications of the tools in their industries and organizations. The situations modeled in the module may not match exactly with the situations faced by the managers, but the objective of the module is to prepare the managers to be ready to build analytical models for rational strategic decision making in any situation, any function, any organization, and any industry.